國立成功大學電機工程學系 教師個人頁面
English Version
林家祥 副教授
地址
電機系館9樓92907室
Email
TEL
06-2757575 ext.62335
實驗室網站連結
高光譜智慧運算實驗室
(R92931/ext.62400-1666)
學經歷
學歷
清華大學電機系(2010)
清華大學通訊所(2016)
經歷
2014，香港中文大學，研究助理
2015-2016，美國維吉尼亞理工，研究助理
2017，香港中文大學，博士後
2017-2018，葡萄牙里斯本大學，博士後
2018，中央大學太空遙測中心，助理教授
2019-2021，成功大學電機系，助理教授
2019-2021，成功大學敏求學院，助理教授
2019，葡萄牙里斯本大學，訪問教授
2022-2023，成功大學數據科學中心(演算法/最佳化組)，組長
2023-present，成功大學國際處(國際關係組)，組長
2022-present，成功大學電機系，副教授
2022-present，成功大學敏求學院，副教授
研究領域
  • 盲訊號處理 / 非監督式機器學習
  • 超光譜影像處理
  • 量子影像處理
  • 快速演算法 / 大數據優化理論
  • 深度學習
  • 凸優化
  • 5G/6G無線通訊
  • 衛星遙測
  • 生物醫學
著作
期刊論文( Journal )
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  1. P.-W. Tang, Chia-Hsiang Lin, and Y.-R. Liu,“Transformer-driven inverse problem transform for fast blind hyperspectral image dehazing,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  2. Chia-Hsiang Lin, T.-H. Lin, and J. Chanussot, “Quantum information-empowered graph neural network for hyperspectral change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  3. J.-T. Lin, and Chia-Hsiang Lin, “SuperRPCA: A collaborative superpixel representation prior-aided RPCA for hyperspectral anomaly detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  4. S.-S. Young, Chia-Hsiang Lin, and Z.-C. Leng, “Unsupervised abundance matrix reconstruction transformer guided fractional attention mechanism for hyperspectral anomaly detection,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
  5. Chia-Hsiang Lin , C.-Y. Hsieh, and J.-T. Lin, “CODE-IF: A convex/deep image fusion algorithm for efficient hyperspectral super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  6. Chia-Hsiang Lin, and S.-S. Young, “Signal subspace identification for incomplete hyperspectral image with applications to various inverse problems,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2024.
  7. Chia-Hsiang Lin, C.-C. Hsu, S.-S. Young, C.-Y. Hsieh, and S.-C. Tai, “QRCODE: Quasi-Residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024.
  8. Chia-Hsiang Lin, S.-H. Huang, T.-H. Lin, and P.-C. Wu, “Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory,” accepted by Nature Communications, 2023.
  9. Chia-Hsiang Lin, M.-C. Chu, and P.-W. Tang, “CODE-MM: Convex deep mangrove mapping algorithm based on optical satellite images,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2023.
  10. Chia-Hsiang Lin, and T.-H. Lin, "Hyperspectral change detection using semi-supervised graph neural network and convex deep learning," accepted by IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023.
  11. Chia-Hsiang Lin, and Y.-Y. Chen, “HyperQUEEN: Hyperspectral quantum deep network for image restoration,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1-20, 2023.
  12. Chia-Hsiang Lin, Y. Liu, C.-Y. Chi, C.-C. Hsu, H. Ren, and T. Q. S. Quek, “Hyperspectral tensor completion using low-rank modeling and convex functional analysis ,” accepted by IEEE Transactions on Neural Networks and Learning Systems, 2022.
  13. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “Optimization-based hyperspectral spatiotemporal super-resolution,”IEEE Access, pp. 37477-37494, 2022.
  14. L. Chen, C.-T. Wu, Chia-Hsiang Lin, R. Dai, C. Liu, R. Clarke, G. Yu, J. E. Van Eyk, D. M. Herrington, and Y. Wang, “swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution,”Bioinformatics, vol. 38, no. 5, pp. 1403-1410, 2022.
  15. Chia-Hsiang Lin, Y.-C. Lin, and P.-W. Tang, “ADMM-ADAM: A new inverse imaging framework blending the advantages of convex optimization and deep learning,”IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-16, 2022.
  16. C.-H. Lee, R. Chang, S.-M. Cheng, Chia-Hsiang Lin, and C.-H. Hsiao, “Joint beamforming and power allocation for M2M/H2H co-existence in green dynamic TDD networks: Low-complexity optimal designs,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4799-4815, 2022.
  17. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, H.-C. Wu, H.-T. Kuo, C.-F. Lin, P. Chen, and P.-C. Wu, “Automatic inverse design of high-performance beam-steering metasurfaces via genetic-type tree optimization,” Nano Letters, vol. 21, no. 12, pp. 4981-4989, Jun. 2021.
  18. Chia-Hsiang Lin, and T.-H. Lin, “All-addition hyperspectral compressed sensing for metasurface-driven miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-15, 2022.
  19. C.-C. Hsu, Chia-Hsiang Lin, C.-H. Kao, and Y.-C. Lin, “DCSN: Deep compressed sensing network for efficient hyperspectral data transmission of miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7773-7789, Sep. 2021.
  20. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Non-negative blind source separation for ill-conditioned mixtures via John ellipsoid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2209-2223, May 2021.
  21. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “An explicit and scene-adapted definition of convex self-similarity prior with application to unsupervised Sentinel-2 superresolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3352-3365, May 2020.
  22. L. Zhuang, Chia-Hsiang Lin, M. A. T. Figueiredo, and J. M. Bioucas-Dias, “Regularization parameter selection in minimum volume hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 9858-9877, Dec. 2019.
  23. Y.-R. Syu, Chia-Hsiang Lin, and C.-Y. Chi, “An outlier-insensitive unmixing algorithm with spatially varying hyperspectral signatures,” IEEE Access, vol. 7, pp.15086-15101, Jan. 2019.
  24. Chia-Hsiang Lin, C.-Y. Chi, L. Chen, D. J. Miller, and Y.Wang, “Detection of sources in non-negative blind source separation by minimum description length criterion,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4022-4037, Sep. 2018.
  25. Chia-Hsiang Lin, R. Wu, W.-K. Ma, C.-Y. Chi, and Y. Wang, “Maximum volume inscribed ellipsoid: A new simplex-structured matrix factorization framework via facet enumeration and convex optimization,” SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1651-1679, Jun. 2018.
  26. Chia-Hsiang Lin, F. Ma, C.-Y. Chi, and C.-H. Hsieh, “A convex optimization based coupled non-negative matrix factorization algorithm for hyperspectral and multispectral data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1652-1667, Mar. 2018.
  27. G. Xu, Chia-Hsiang Lin, W. Ma, S. Chen, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” IEEE Access, vol. 5, pp. 13601-13616, Mar. 2017.
  28. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1946-1961, Apr. 2016.
  29. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, F.-S. Yang, C.-Y. Chi, and Y. Wang, “Convex optimization-based compartmental pharmacokinetic analysis for prostate tumor characterization using DCE-MRI,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 707-720, Apr. 2016.
  30. Chia-Hsiang Lin, W.-K. Ma, W.-C. Li, C.-Y. Chi, and A. Ambikapathi, “Identifiability of the simplex volume minimization criterion for blind hyperspectral unmixing: The no pure-pixel case,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5530-5546, Oct. 2015.
會議論文( Conference )
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  1. Chia-Hsiang Lin, S.-S. Young, C. Liu, L.-Y. Chang, and T.-Y. Liao, “Image resolution enhancing of Sentinel-2 red edge bands via Pléiades-1 multispectral data and fast convex deep learning,” SPIE Asia-Pacific Remote Sensing Symposium, Kaohsiung, Taiwan, Dec. 2-4, 2024.
  2. S.-S. Young, Chia-Hsiang Lin, J.-Y. Chen, and J.-K. Huang, “HyperQUEEN-CD: Quantum neural network for unsupervised hyperspectral change detection,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  3. G.-J. Wei, Chia-Hsiang Lin, and S.-M. Hsu, “A channel-wise quantum attention mechanism for RGB and hyperspectral image super-resolution,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  4. (Invited Paper) S.-M. Hsu, T.-H. Lin, and Chia-Hsiang Lin, “HyperQUEEN-MF: Hyperspectral quantum deep network with multi-scale feature fusion for quantum image super-resolution,” accepted by IEEE SAM, Corvallis, OR, USA, July 8-11, 2024.
  5. Chia-Hsiang Lin, C.-Y. Kuo, and S.-S. Young, “Quantum adversarial learning for hyperspectral remote sensing,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024
  6. Chia-Hsiang Lin, S.-S. Young, L.-Y. Chang, and Cynthia S.J. Liu, “Synthesis of high-resolution FORMOSAT-8 satellite image using fast convex deep learning algorithm,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024
  7. S.-S. Young, *Chia-Hsiang Lin, and J.-T. Lin, “CiDAR-Former: Cosine-weighting deep abundance reconstruction transformer for fast unsupervised hyperspectral anomaly detection,” accepted by IEEE WHISPERS, Athens, Greece, Oct. 31-Nov. 2, 2023.
  8. T.-H. Lin, and *Chia-Hsiang Lin, and S.-S. Young, “GNN-based small-data learning with area-control mechanism for hyperspectral satellite change detection,” accepted by Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Taipei, Taiwan, Oct. 31-Nov. 3, 2023.
  9. Chia-Hsiang Lin, and Y.-Y. Chen, “Quantum deep hyperspectral satellite remote sensing,” IEEE IGARSS, Pasadena, California, July 16-21, 2023.
  10. Chia-Hsiang Lin, M.-C. Chu, and H.-J. Chu, “High-dimensional multiresolution satellite image classification: An approach blending the advantages of convex optimization and deep learning,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  11. Chia-Hsiang Lin, T.-H. Lin, T.-H. Lin, and T.-H. Lin, “Fast reconstruction of hyperspectral image from its RGB counterpart using ADMM-Adam theory,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  12. Y. Liu, Chia-Hsiang Lin, and Y.-C. Kuo, “Low-rank representation with morphological-attribute-filter based regularization for hyperspectral anomaly detection,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  13. P.-W. Tang, and Chia-Hsiang Lin, “Hyperspectral dehazing using ADMM-Adam theory,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  14. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “A fast multidimensional data fusion algorithm for hyperspectral spatiotemporal super-resolution,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  15. T.-H. Lin, and Chia-Hsiang Lin, “Single hyperspectral image super-resolution using ADMM-Adam theory,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  16. J.-T. Lin, and Chia-Hsiang Lin, “Real-time hyperspectral anomaly detection using collaborative superpixel representation with boundary refinement,”IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  17. C.-H. Yu, Z.-C. Leng, Y. Liu, J.-Y. Huang, Chia-Hsiang Lin, and T.-Y. Tu, “A total solutioning workflow for sample processing and precise nuclei quantification in 3D tumor spheroids using unsupervised algorithm,”World Congress of Biomechanics, Taipei, Taiwan, Jul. 10-14, 2022.
  18. A. Hassanfiroozi, Chia-Hsiang Lin, J.-T. Lin, and P.-C.Wu, “High-performance metasurfaces for wavefront engineering,”Materials Research Society Fall Meeting and Exhibit, Boston, MA, USA, Nov. 28 - Dec. 3, 2021.
  19. C.-H. Kao, Chia-Hsiang Lin, S.-W. Jian, and P.-Y. Lin, “Solving hyperspectral single image super-resolution via fusion-based inverse problem transform,” The 34th IPPR Conference on Computer Vision, Graphics, and Image Processing, Taipei, Taiwan, Aug. 22-24, 2021. (“Outstanding Paper Award”)
  20. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, Y.-C. Cheng, A. Hassanfiroozi, H.-C. Wu, H.-T. Kuo, and P.-C. Wu, “Toward high-performance plasmonic metasurfaces: From forward to inverse design approach,” SPIE Optics and Photonics, San Diego, CA, USA, Aug. 1-5, 2021.
  21. Chia-Hsiang Lin, C.-Y. Sie, P.-Y. Lin, and J.-T. Lin, “Fast unsupervised spatiotemporal super-resolution for multispectral satellite imaging using plug-and-play machinery strategy,” IEEE IGARSS, Brussels, Belgium, Jul. 11-16, 2021.
  22. Chia-Hsiang Lin, Y.-C. Lin, P.-W. Tang, and M.-C. Chu, “Deep hyperspectral tensor completion just using small data,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.
  23. Chia-Hsiang Lin, and P.-W. Tang, “Inverse problem transform: Solving hyperspec- tral inpainting via deterministic compressed sensing,”IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  24. Chia-Hsiang Lin, and Y. Liu, “Blind hyperspectral inpainting via John ellipsoid,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  25. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, and P.-C. Wu, “Inverse design of non- periodical metasurfaces via high-performance automatic optimization,” in Proc. Op- tics & Photonics Taiwan International Conference (OPTIC), Taipei, Taiwan, Dec. 3-5, 2020.
  26. C.-C. Hsu, W.-H. Zheng, H.-T. Yang, Chia-Hsiang Lin, and C.-H. Kao, “Rethinking relation between model stacking and recurrent neural networks for social media prediction,” in Proc. ACM Multimedia (MM), Seattle, WA, USA, Oct. 12-16, 2020. (“Invited Paper”) (“Top Performance Award”)
  27. Y.-C. Hung*, Chia-Hsiang Lin*, F.-Y. Wang, and S.-H. Yang, “Penetrating tera- hertz hyperspectral unmixing via Lo ̈wner-John ellipsoid: An unsupervised algorithm,” in Proc. IRMMW-THz, Buffalo, NY, USA, Sep. 13-18, 2020. (*Contributed Equally)
  28. C.-C. Hsu, Y.-C. Lin, C.-H. Kao, and Chia-Hsiang Lin, “Deep joint compression and super-resolution low-rank network for fast hyperspectral data transmission,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  29. T.-H. Lin, Chia-Hsiang Lin, Y. Liu, and C.-H. Kao, “A simple spatial-spectral proximal compression method for high-dimensional imagery with proximal computing based blind reconstruction,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  30. C.-Y. Sie, Chia-Hsiang Lin, P.-W. Tang, and Y.-C. Lin, “Solving the algebraic hyperspectral inpainting problem: A fast hyperplane geometry based approach,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”) (“Outstanding Paper Award”)
  31. Chia-Hsiang Lin, J. M. Bioucas-Dias, T.-H. Lin, Y.-C. Lin, and C.-H. Kao, “A new hyperspectral compressed sensing method for efficient satellite communications,” in Proc. IEEE SAM, Hangzhou, China, June 8-11, 2020. (“Invited Paper”)
  32. W.-C. Zheng, K.-H. Tseng, and Chia-Hsiang Lin, “Unsupervised change detection using convex relaxation and dynamic threshold selection in remotely sensed images,” American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA, Dec. 9-13, 2019.
  33. C.-C. Hsu, and Chia-Hsiang Lin, “Dual reconstruction with densely connected residual network for single image super-resolution,” in Proc. IEEE ICCV, Seoul, Korea, Oct.27 - Nov. 2, 2019. (“Invited Paper”)
  34. C.-H. Wang, K.-H. Tseng, and Chia-Hsiang Lin, “Waterline detection using fusion based super-resolution of multispectral satellite image with self-similarity,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  35. T.-Y. Lin, H. Ren, and Chia-Hsiang Lin, “Bathymetry estimation via convex geometry in multispectral satellite imagery: A case study in Dongsha Atoll,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  36. W.-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, T.-H. Lin, C.-H. Wang, and C.-Y. Chi, “Unsupervised change detection in multitemporal multispectral satellite images: A convex relaxation approach,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019.
  37. C.-H. Wang, Chia-Hsiang Lin, J. M. Bioucas-Dias, W.-C. Zheng, and K.-H. Tseng, “Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019. (“Interactive Session Prize Paper Award”)
  38. W-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, and T.-H. Lin, “Criterion design and large-scale optimization algorithm for blind change detection in multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  39. C.-H. Wang, Chia-Hsiang Lin, and K.-H. Tseng, “Patch similarity guided super-resolution algorithm for fusing panchromatic and multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  40. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Linear spectral unmixing via matrix factorization: Identifiability criteria for sparse abundances,” in Proc. IEEE IGARSS, Valencia, Spain, Jul. 23-27, 2018.
  41. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “New theory for unmixing ill-conditioned hyperspectral mixtures,” in Proc. IEEE SAM, Sheffield, UK, Jul. 8-11, 2018. (“Invited Paper”)
  42. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Provably and robust blind source separation of ill-conditioned hyperspectral mixtures,” in Proc. IEEE SSP, Freiburg, Germany, Jun. 10-13, 2018.
  43. G. Xu, Chia-Hsiang Lin, W. Ma, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” in Proc. IEEE ICC, Paris, France, May 21-25, 2017.
  44. W.-K. Ma, Chia-Hsiang Lin, W.-C. Li, and C.-Y. Chi, “When can the minimum volume enclosing simplex identify the endmembers correctly when there is no pure pixel?,” in Proc. IEEE WHISPERS, Tokyo, Japan, Jun. 2-5, 2015.
  45. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based MVES algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Brisbane, Australia, Apr. 19-24, 2015.
  46. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, and C.-Y. Chi, “Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images,” in Proc. IEEE WHISPERS, Gainesville, Florida, USA, Jun. 25-28, 2013. (“Invited Paper”)
  47. Chia-Hsiang Lin, A. Ambikapathi, W.-C. Li, and C.-Y. Chi, “On the endmember identifiability of Craig’s criterion for hyperspectral unmixing: A statistical analysis for three-source case,” in Proc. IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.
專利
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其他
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  1. (Book) C.-Y. Chi, W.-C. Li, and Chia-Hsiang Lin, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, Boca Raton, FL, Feb. 2017. (432 pages)
  2. 電子工業出版社，「信號處理與通信中的凸優化：從基礎到應用」（作者：祁忠勇、李威錆、林家祥）（譯者：陳翔、沉超），2020年12月出版。
研究計劃
  1. 超皇后：深度量子高光譜太空遙測，國科會(2030跨世代年輕學者方案「新秀學者研究計畫」)，2023~2027
  2. 應用智慧運算方法進行衛星紅邊波段影像模擬研究，國家太空中心，2023~2024
  3. 基於凸幾何與大數據優化之前瞻盲蔽訊號源分離與高光譜超解析度成像，科技部(愛因斯坦培植計畫)，2018~2023
  4. 前瞻衛星成像之數學理論與超穎光柵設計，教育部，2019~2022
  5. 建構動態地電阻譜：整合數值解析法暨災防應用(共同主持)，教育部，2019
開授課程
107學年度下學期
108學年度上學期
108學年度下學期
109學年度上學期
109學年度下學期
110學年度上學期
110學年度下學期
111學年度上學期
111學年度下學期
112學年度上學期
113學年度上學期
指導學生
本學年度 實驗室成員
博士班
林子亘
湯柏緯
林昭廷
甯子釗
楊斯盛
共同指導博士班
許奭民
碩士班
衛廣杰
鍾怡萱
郭辰瑜
李維恆
陳之硯
范城瑋
梁詠升
林瑋浚
黃建凱
林伯育
共同指導碩士班
柳辰諭
碩士生
梁菁芸
專題生
楊珽鈞
施尚均
楊秉融
張育榮
王千豪
廖先進
已畢業學生
碩士
109
高齊鴻   謝承育   林彥呈
110
朱曼君   簡韶葦
111
林庭萱   謝承穎   余冠篁   賴宥瑜
學士
109
李奕勳   甯子釗
110
黃筱晴   林宛萱   張百顓   陳右耀
111
吳哲郁   徐忠敬   何彥霆
特殊榮譽
  1. 2024年 財團法人成電文教基金會創惟科技公司創惟論文卓越獎
  2. 2024年 112學年度教學創新與大學社會責任-教學創新：EMI教學-教學優良教師
  3. 2024年 113年中華民國青年獎章（學術拔萃類）
  4. 2023年  國科會2030跨世代年輕學者方案「新秀學者研究計畫」
  5. 2023年  國立成功大學111學年度教學優良獎
  6. 2023年  國立成功大學電機工程學系111學年度輔導優良導師獎
  7. 2023年 110年度國科會電信學門計畫成果「優良海報展示獎」
  8. 2022年 中國電機工程學會優秀青年電機工程師獎
  9. 2022年 國科會未來科技獎
  10. 2022年 IPPR 電腦視覺、圖學、暨影像處理會議最佳論文獎
  11. 2022年 109年度科技部電信學門計畫成果「優良海報展示獎」
  12. 2021年 國際電機電子工程師學會中華民國第一分會(IEEE Tainan Section) Best Young Professional Member Award
  13. 2021年 IPPR 電腦視覺、圖學、暨影像處理會議最佳論文獎
  14. 2021年 108年度科技部電信學門計畫成果「特優獎」（愛因斯坦計畫）
  15. 2020年 ACM Multimedia 2020社群媒體預測挑戰賽Top Performance Award
  16. 2020年 IPPR 電腦視覺、圖學、暨影像處理會議最佳論文獎
  17. 2020年 IEEE地球科學與遙測學會IEEE Geoscience and Remote Sensing Society (IEEE GRS-S) Prize Paper Award 
  18. 2019年 IEEE國際電腦視覺會議IEEE International Conference on Computer Vision (IEEE ICCV) 影像超解析挑戰賽第三名
  19. 2018年 科技部年輕學者 MOST Young Scholar Fellowship (愛因斯坦培植計畫) 
  20. 2016年 IEEE地球科學與遙測學會IEEE Geoscience and Remote Sensing Society (IEEE GRS-S) 最佳博士論文獎
  21. 2016年 IPPR 電腦視覺、圖學、暨影像處理會議 佳作博士論文獎
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